Cancer
arises from accumulation of somatic mutations and accompanying
evolutionary selection for growth advantage. During the evolutionary
process, an ancestor clone branches into multiple clones, yielding
intratumor heterogeneity. However, principles underlying intratumor
heterogeneity have been poorly understood. Here, to explore the
principles, we built a cellular automaton model, termed the BEP model,
which can reproduce the branching cancer evolution in silico. We then
extensively searched for conditions leading to high intratumor
heterogeneity by performing simulations with various parameter settings
on a supercomputer. Our result suggests that multiple driver genes of
moderate strength can shape subclonal structures by positive natural
selection. Moreover, we found that high mutation rate and a stem cell
hierarchy can contribute to extremely high intratumor heterogeneity,
which is characterized by fractal patterns, through neutral evolution.
Collectively, This study identified the possible principles underlying
intratumor heterogeneity, which provide novel insights into the origin
of cancer robustness and evolvability.

Abstract
The sequential changes occurring with cancer progression are now being
harnessed with therapeutic intent. Yet, there is no understanding of the chemical
thermodynamics of proteomic changes associated with cancer progression/
cancer stage. This manuscript reveals a strong correlation of a chemical
thermodynamic measure (known as Gibbs free energy) of protein-protein
interaction networks for several cancer types and 5-year overall survival and
stage in patients with cancer. Earlier studies have linked degree entropy of
signaling networks to patient survival data, but not with stage. It appears that
Gibbs free energy is a more general metric and accounts better for the
underlying energetic landscape of protein expression in cells, thus correlating
with stage as well as survival.

This is an especially timely finding because of improved ability to obtain and
analyze genomic/ proteomic information from individual patients. Yet, at least at
present, only candidate gene imaging (FISH or immunohistochemistry) can be
used for entropy computations. With continually expanding use of genomic information in clinical medicine, there is an ever-increasing need to understand
the thermodynamics of protein-protein interaction networks.